Codes of the paper "Learning Extremely Shared Middle-level Image Representation for Scene Classification"
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feature_extraction_imagenet
include
MITindoor_classification.m
README.md
TestImages.txt
TrainImages.txt
dictionary_learning_MIT_shared.m
exp_MITindoorft.m
extr_deep_img_feat.m
extr_patch_dfeat_linux.m
matcaffe_demo_linux.m
norm_matrix.m
retr_database_dir.m
sceneft_CNNfeat.m
score_one_image.m
score_one_image1.m
shared.m

README.md

Learning Extremely Shared Middle-level Image Representation for Scene Classification

Introduction

The implemention codes on MIT-indoor 67 dataset.

A novel method to learn very compact and discriminative image representation.

Dependencies

The MIT-indoor 67 dataset can be downloaded at this website and should be put under the "data" path.

The caffe is needed to be installed.

The CNN model can be downloaded at the caffe model zoo and be put under the "feature_extraction_imagenet" path.

VLFeat and LIBLINEAR are also needed.

Trained models

We put 4 different trained models "best_2.mat", "best_4.mat", "best_6.mat", "best_8.mat" under the "data" path, which correspond to 2 patterns, 4 patterns, 6 patterns, 8 patterns per class respectively.

Testing

Run the "MITindoor_classification.m" to test the trained model.

Training

Run the "exp_MITindoorft.m" to train the model.

License

The code is released under the MIT License.

Citing paper

If you find the work is useful in your research, please considering citing:

@article{tang2016learning,
    title = {Learning extremely shared middle-level image representation for scene classification},
    author = {Tang, Peng and Zhang, Jin and Wang, Xinggang and Feng, Bin and Roli, Fabio and Liu, Wenyu},
    journal = {Knowledge and Information Systems},
    pages = {1--22},
    year = {2016},
    publisher = {Springer}
}